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An enhanced possibilistic programming model with fuzzy random confidence-interval for multi-objective problem

Arbaiy, Nureize and Samsudin, Noor Azah and Mustapa, Aida and Watada, Junzo and Lin, Pei Chun (2018) An enhanced possibilistic programming model with fuzzy random confidence-interval for multi-objective problem. In: Studies in Computational Intelligence. Innovative Computing, Optimization and Its Applications, 741 . Springer, pp. 217-235. ISBN 9783319669847

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Abstract

Mathematical models are established to represent real-world problems. Since the real-world faces various types of uncertainties, it makes mathematical model suffers with insufficient uncertainties modeling. The existing models lack of explanation in dealing uncertainties. In this paper, construction of mathematical model for decision making scenario with uncertainties is presented. Primarily, fuzzy random regression is applied to formulate a corresponding mathematical model from real application of a multi-objective problem. Then, a technique in possibilistic theory, known as modality optimization is used to solve the developed model. Consequently, the result shows that a well-defined multi-objective mathematical model is possible to be formulated for decision making problems with the uncertainty. Indeed, such problems with uncertainties can be solved efficiently with the presence of modality optimization.

Item Type: Book Section
Uncontrolled Keywords: Fuzzy random regression; possibilistic programming; confidence-interval; modality optimization
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 31 Jul 2019 00:58
Last Modified: 31 Jul 2019 00:58
URI: http://eprints.uthm.edu.my/id/eprint/11357
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